{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-05-16T02:49:31.994561Z",
     "start_time": "2025-05-16T02:49:31.990007Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score\n",
    "# 设置支持中文的字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用于显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用于显示负号"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T02:46:51.801124Z",
     "start_time": "2025-05-16T02:46:51.796196Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 直接定义西瓜数据集4.0\n",
    "data = np.array([\n",
    "    [0.697, 0.460], [0.774, 0.376], [0.634, 0.264], [0.608, 0.318],\n",
    "    [0.556, 0.215], [0.403, 0.237], [0.481, 0.149], [0.437, 0.211],\n",
    "    [0.666, 0.091], [0.243, 0.267], [0.245, 0.057], [0.343, 0.099],\n",
    "    [0.639, 0.161], [0.657, 0.198], [0.360, 0.370], [0.593, 0.042],\n",
    "    [0.719, 0.103], [0.359, 0.188], [0.339, 0.241], [0.282, 0.257],\n",
    "    [0.748, 0.232], [0.714, 0.346], [0.483, 0.312], [0.478, 0.437],\n",
    "    [0.525, 0.369], [0.751, 0.489], [0.532, 0.472], [0.473, 0.376],\n",
    "    [0.725, 0.445], [0.446, 0.459]\n",
    "])"
   ],
   "id": "583e97091cbe5d68",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T02:47:02.366933Z",
     "start_time": "2025-05-16T02:47:02.362587Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "data_scaled = scaler.fit_transform(data)"
   ],
   "id": "a99e15ab9b0351a1",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T02:47:12.786836Z",
     "start_time": "2025-05-16T02:47:12.782818Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 设置三组不同的K值\n",
    "k_values = [2, 3, 4]"
   ],
   "id": "92a1f387ef9553ee",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-16T02:49:35.491676Z",
     "start_time": "2025-05-16T02:49:35.069539Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 实验比较\n",
    "for k in k_values:\n",
    "    # 训练高斯混合模型\n",
    "    gmm = GaussianMixture(n_components=k, random_state=42)\n",
    "    gmm.fit(data_scaled)\n",
    "    predictions = gmm.predict(data_scaled)\n",
    "    sse_value = gmm.score(data_scaled) * -len(data_scaled)  # GMM 的 score 方法返回的是 log-likelihood，取负值并乘以数据点数量得到 SSE\n",
    "    silhouette_avg = silhouette_score(data_scaled, predictions)\n",
    "    print(f\"K={k}\")\n",
    "    print(\"预测结果：\")\n",
    "    print(predictions)\n",
    "    print(f\"SSE: {sse_value}\")\n",
    "    print(f\"轮廓系数: {silhouette_avg}\")\n",
    "    # 可视化\n",
    "    plt.figure()\n",
    "    for i in range(k):\n",
    "        cluster_data = data_scaled[predictions == i]\n",
    "        plt.scatter(cluster_data[:, 0], cluster_data[:, 1], label=f\"类别{i + 1}\")\n",
    "    plt.scatter(gmm.means_[:, 0], gmm.means_[:, 1], c='red', marker='x', label=\"中心点\")\n",
    "    plt.title(f\"K={k}\")\n",
    "    plt.legend()\n",
    "    plt.show()"
   ],
   "id": "8824b9bd5760031d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=2\n",
      "预测结果：\n",
      "[1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1]\n",
      "SSE: 81.6288565607653\n",
      "轮廓系数: 0.3400934107122955\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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F2vbt22fChw46fvfdd83sJw0vd9xxh9mnD12gUYOJBhQdrHz//febmUD+pQJ0irKGlGuvvVZWrVpV5t/R1hqtufL888+b52jriwYbuOwF1Qj/OeUFFagWiv9Zh5Bi8RvAl19+aeqmTJs2zdRI0Zomd955pxw5ciRwjpYUvvXWW2XFihXywgsvmGM62FbP17EsOstHB9pqPZVNmzYFWkx0/MoPP/wgv/rVr0wI0odOcYaL8IIKOI/if1ahu6eqN4By66Q8FbMf2K+//tp0v/z3f/+3bNy40XTH3HjjjebYBx98IL///e9Nt4/u0wJsWqjtiy++MCX0zznnHNO9o0XZevfuLbfdVrpmibbMaNi57LLLpH///mafjlnRB1xOfw51mnEcCw4CnkfxP+8uMGjLAkWOLXoX54qz2qWjhdY0hGgLSXnVYrWrZvbs2aYc/ty5cyNacFD/N5dX1M0JLDAIAHa+p7h9gUFaUixL1JEUt9Ny+H/4wx8C25GsiByrgAIAiBDLWESNMSkAAMSrinl4DS5/FXMWMCwXIQUAAI9WMXc7QgoAALHEMhbVRkgBACCWWMai2ggpHqUzfoJL6QOO0Obo/E9ENi8p/UjzNFA1lrGoNkKKS9xwww0ycaL2aUZm2bJlcsUVV8iBAwei+ne0YJwuYlhe6HHxbHU4QQf2PddV5OUhIn+9o/SjbjPgD6gc6wJVGyHFJerWrWvW84nUsGHDpHv37vKv//qvUf07WuekoKDArKLsf+gc9tTUVNm+fXs1rhyewMwEwPVVzN2IOikVKSwUKS4Wadu27LHdu0UaN9bliGPyTy9dulTuvffekH26To8Wb1u0aFHI/v/93/+Vf/mXfyn360yfPt2s3fPtt99K+/btI/73W7duLbv1ewQimpmQUjozQSvf8iILWFXF3O0IKRUFlNxckYICkQ8/FDmzaJ+xa5fIlVeKZGZq30hMgoqWttcWDC2L73fLLbfIueeeK4888kiVBdp0UcIff/xRBg8ebCrB+qvWamVYKsIipjMTKCUOVIxlLKJGd095tAVFA8o//1kaSDSYBAcU3a/H9bwY0EUBoz13wYIFcvXVV5vPlyxZIosXLzaf+wPKyZMnpVu3bvLOO++UO95Fw4sGo+HDh5sVkf1dPVq2WMOQ7kOSYmYC4HwV8wtuLP1IQKkUIaU82sWjLSjnnPNzUPn0058Diu7X4+V1BTmgOiXsP//880B5fA0c2jUUPqblrrvukjFjxphVkcOPTZo0yaygHP7wj0OhBSaJMTMBQIIQUiqiXTzBQeWyy0IDSnAXkMNOnz4d9blffvml5OTkVBpy7rvvPsnMzJTf/OY3UbfcsPZPEmNmAoAEIaRURoNI2EBVsx3DgOIPDfv27ZOzzz478NDBtM8991zIvmbNmpmQol0x2pJyySWXVPl1Z8yYIV9//bUUB3VV6RiYZ555Rpo3b17mcd555wXGsyBJMTMBQIIwcLYyOgbl1ltD9+l2jFtSbrzxRvMIVt7AWb8333xTfvrpJ7n00qr/ku3fv7989tlngdaTXbt2yf333y9//etfTS0Uf5dRSUmJ6QbShwYh7SJq2LChY98jXIaZCQASgJaUigQPktUunlWrQseo+AfTWqBv376mCJvWM6nI1q1b5brrrjOtLsHdO7NmzZIHH3zQfH7VVVfJ+++/bz4fPXq0vPDCC+bzOXPmmMG1FHNLchpE7s0T+dXbIjfML/1472YCCoCYIaSUR2uEhA+S1VaK8MG0cawloq0cFY1V0Vk4AwcODGxrCPnnP/8pO3fuNPVO9KGtLatWrQppDTl06JAsXLgwEFI07MydO9d8rkXgnnjiCSkqKjKDbb/55htzLpIcMxMAxBHdPeXRQm1aB0UFd+34B9P666ToeXGiY0i09kkkrrzyStMKouNW/LQbR0NH8ADYLVu2mO6fAQMGmO2bb745MNV40KBBpvXk+++/N19Hu5m0awgAgHhJ8bm4DV//ytc6HoWFhWW6OnSgpxYyy87Ort702QRWnHWbGt9rAEBSKark/TsYLSkV0QBSUQiJUX0UAADwM8akAAAAKxFSAACAlTwfUlw85MY1oqmQCwBApDw7JkWLkOlMlgMHDkiLFi0o6x6jAKgzjvQe67TnevXqJfqSAAAe4tmQolNu27Zta2qE7NixI9GX42kNGjSQ9u3bR7V6MwAASRtSVKNGjaRTp05y8uTJRF+KeDkM6orLtFQBAJzm6ZDifxP1r0cDAADcg/Z5ALCRFpSsaOkN3a/HAY8jpACAbTSA5OaKXHFF2cVMdVv363GCCjyOkAIAttElOQoKyq66Hrw6ux7X8wAPczSkHDx40KzfEulsmo8++kg6d+4szZs3l5kzZzp5KQDgXrr0Rviq659+WnZ1dpbogMfVcjKgDBkyJOKAorU1hg0bJiNHjpTVq1fLa6+9Jh988IFTlwMA7uZfdd0fVC67LDSg+FdnBzzMsZAyYsQIGTVqVMTnayhp3bq1PPTQQ2aa8MMPPyzz58936nIAwP00iCxaFLpPtwkoSBKOhZR58+bJ+PHjIz5/06ZN0q9fv0B9jZ49e8r69esrfc6JEyfM8s7BDwDwLB2Dcuutoft0O3wwLeBRjoUUHYsSDQ0Ywc9JS0uTvXv3Vvqc6dOnS3p6euDRjr8mAHhV8CBZ7eJZtSp0jApBBUkgYbN7tEppampqYLt+/fpSUlJS6XMmT54shYWFgccufkkBeJHWQQkfJHvppWUH01ZURwXwiIRVnM3IyDCDZ/2Ki4urXKBOQ01wsAEAT2rcWCQzs/Tz4EGy/sG0GlD0uJ4HeFjCQkpOTo786U9/Cmxv2LBB2rRpk6jLAQB7pKeLLF9eWgclfJqxBpWPPioNKHoe4GEx7+7RsSflLfCn049XrVol7733njn+zDPPyKBBg2J9OQDgDhpAKqqDovsJKEgCMQ8p3bp1k2XLlpXZrwXcZs2aJddcc420bNlStm3bJlOmTIn15QAAAJdI8fl8vkReQH5+vmzdulX69OkjjRo1irqVRmf56CBanR0EAADsF+n7d8LGpPjpNORopy8DAADvY4FBAABgJUIKAACwEiEFAABYiZACAACsREgBAABWIqQAAAArEVIAAICVCCkAAMBKCS/mBsBSp0+J7PxU5Oh3Io1ainS4VKRW7URfFYAk+v0npAAoa8ubIssnihTt/XlfWmuR3KdFugxL5JUBSKLff7p7AJR9gfrLbaEvUKpoX+l+PQ7Am7bY9ftPSAEQ2sSrf0FJeeuOntm3fFLpeQC85bR9v/+EFAA/0z7o8L+gQvhEivaUngfAW3ba9/vPmBTYy5KBW0lF77WT5wFwj6P2/f4TUmAniwZuJRUNg06eF28EW8BTv/+EFNg7cCu8X9Q/cOumVwgqsaJv6hoG9V6X2y+dUnpcz7MNwRbw3O8/Y1JgFwsHbiUVbXXQN3UjJezgme3cp+xrnbBsRgLgSrXs+/0npMAuFg7cSjra6qCtVWmtQvfrX1A2tmIRbAHP/v7T3QO7WDhwKynpC9EvBrtjfEc0wTa7TxwvDHCpLvb8/hNSYBcLB24lLX1BcsObOsEW8OzvP909sHPgVpn+0OCBW23sHLiJxCDYAp5FSIFdLBy4BcsRbAHPIqTAPpYN3ILlCLaAZ6X4fL7yhsS7QlFRkaSnp0thYaGkpaUl+nLgNApzocZ1UtqUBhSCLeDK928GzsJelgzcgktYNCMBgDMIKQC8g2ALeApjUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWInZPQAAOIHaTo4jpAAAEJNigq1LqyFTTLDa6O4BAKCmAeUvt4UGFFW0r3S/Hke1EFIAAKhJF4+2oEh5K8yc2bd8Uul5iBohBQCA6tIxKOEtKCF8IkV7Ss9D1AgpAABUlw6SdfI8xC6k5OXlSU5OjjRt2lQmTJggkSyw3K1bN0lJSQk8xowZ4+QlAQAQOzqLx8nzEJuQcuLECRk6dKhcdNFFsm7dOtmyZYssXLiw0ueUlJTI9u3bpaCgQA4fPmwezz//vFOXBABAbOk0Y53FIykVnJAiktam9DwkLqS88847UlhYKDNnzpSOHTvKtGnTZP78+ZU+Z8OGDaYlpUWLFtKkSRPzOOuss5y6JAAAYkvroOg0YyM8qJzZzn2KeimJDimbNm2SXr16SYMGDcy2hg9tTanM2rVrZffu3YGQMm7cONMiUxE9VlRUFPIAACChtA7KTa+IpLUK3a8tLLqfOimJDykaGLKzswPbOr6kdu3apgunItu2bZPLL79cVq5cKStWrJB3331XZs2aVeH506dPl/T09MCjXbt2Tl0+AADVp0Hk3jyRX70tcsP80o/3biag1FCKL5LRrRGYOHGinDx50nT3+GmIWLNmjbRp0yair/HKK6/InDlzzJiWilpSgltaNBjpv6HdTGlpaQ58FwAAINb0/VsbG6p6/3asLH5GRoaZ3ROsuLhY6tWrF/HXyMzMlD179lR4PDU11TwAAID3Odbdo1OPV69eHdjOz883rR4aXirSu3dv2bVrV2Bbn9+hQwenLgkAALiYYyGlb9++pvlmwYIFZltn9wwYMMCMSzly5IicOlW2JPD5558vY8eOlc8++0xefvllmTFjhhk8CwAA4NiYFPXmm2/KyJEjzTTiWrVqyYcffihdunQxg2h1uvGFF14Ycr6Gl9tvv90MmtWuHh3XEk1IibRPCwAA2CPS929HQ4rav3+/rF+/3kxHbtasmcQSIQUAAPeJ+8BZv6ysLBk8eLDTXxYAACQZFhgEAABWIqQAAAArEVIAAICVHB+TAgCwzOlTIjs/FTn6nUijlqUr8rLgHVyAkAIAXrblTZHlE0WK9oYufKcr97KuDCxHdw8AeDmg/OW20ICiivaV7tfjgMUIKQDg1S4ebUGR8kphndm3fFLpeYClCCkA4EU6BiW8BSWET6RoT+l5gKUYkwKg2k6d9sna/O+loPi4ZDauLz2zM6R2rZREXxaUDpJ18jwgAQgpAKpled4+efStLbKv8HhgX6v0+jJ1aBfJ7doqodcGKZ3F4+R5QALQ3QOgWgFl3KtfhAQUtb/wuNmvx5FgOs1YZ/FIRS1bKSJpbUrPAyxFSAEQdRePtqBUMhzTHNfzkEBaB0WnGRvhQeXMdu5T1EuB1QgpAKKiY1DCW1CCaTTR43oeEkzroNz0ikhaWPebtrDofuqkwHKMSQEQFR0k6+R5iDENIr8YTMVZuBIhBUBUdBaPk+chDjSQZPdJ9FUAUaO7B0BUdJqxzuKpZDimOa7nAUBNEFIAREXroOg040qGY5rj1EsBUFOEFABR0zooL97SQ7LSQ7t0dFv3UycFgBMYkwKgWjSIDOySRcVZADFDSAFQbRpIendslujLAOBRdPcAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAALASIQUAAFiJkAIAAKxESAEAAFYipAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAViKkAAAA74eUvLw8ycnJkaZNm8qECRPE5/NV+ZwlS5ZIhw4dpHXr1rJ48WInLwcAALiYYyHlxIkTMnToULnoootk3bp1smXLFlm4cGGVoWb06NHy0EMPyYoVK+Thhx+Wbdu2OXVJAADAxRwLKe+8844UFhbKzJkzpWPHjjJt2jSZP39+pc/5r//6L+nXr5+MGTNGLrjgArnnnntk0aJFTl0SAABwMcdCyqZNm6RXr17SoEEDs92tWzfTmlLVc/r37x/Y7tmzp6xfv77S1pqioqKQBwAA8CbHQooGhuzs7MB2SkqK1K5dWw4fPhzxc9LS0mTv3r0Vnj99+nRJT08PPNq1aydOO3XaJ6u3H5I3Nu4xH3UbAADEXx3HvlCdOpKamhqyr379+lJSUmIG0kbyHP/5FZk8ebLcd999ISHHyaCyPG+fPPrWFtlXeDywr1V6fZk6tIvkdm3l2L8DAADi2JKSkZEhBw4cCNlXXFws9erVi/g5VZ2vgUZbW4IfTgaUca9+ERJQ1P7C42a/HgcAAC4MKTr1ePXq1YHt/Px8M4ZEg0ikz9mwYYO0adNG4k27dLQFpbyOHf8+PU7XDwD8jO5xuCak9O3b13S/LFiwwGzr7J4BAwaYcSlHjhyRU6dOlXnODTfcIK+//rps3rxZjh49KnPmzJFBgwZJvK3N/75MC0ow/bXT43oeAKC09fnyp/8uI+etkd++vtF81G1anWFlSNHxJTqlWKcRN2/eXN544w15+umnzTEdk6JBJFz37t3lt7/9rVx88cWmBUUDzd133y3xVlB83NHzAMDL6B6H6wbOqmHDhsn27dvNNGKdjtysWTOzv7LKs08++aQp6LZnzx654oorKh2TEiuZjes7eh4AeFVV3eMpZ7rHB3bJktq1dAuwaO2erKwsGTx4cCCgRKJLly4ycODAhAQU1TM7w8ziqejXSffrcT0PAJIZ3eOIJxYYFDFpX6cZq/Cg4t/W4/H6q4DBaABsRfc4XNvd42ZaB+XFW3qUqZOSFec6KdRqAWAzuscRT4SUIBoCtB9Vmyn1rwD9JdMunni1oPgHo4W3m/gHo2mIIqgASCR/97i+LpXXxpty5o87usfhBLp7wmgg6d2xmVx7YRvzMZ5dPNRqAWA727rH4W2EFEswGA2A27rHtcUkmG7T4gsn0d1jCQajAXCTRHePIzkQUizBYDQAbu0eB2KF7h5LUKsFAIBQhBRLMBgNAIBQhBSLMBgNAICfMSbFMgxGAwCgFCHFQgxGAwCAkIIY0aJztAYBAGqCkALHsf4QAMAJDJxFTNYfCq+e619/SI8DABAJQgockwzrD+m1r95+SN7YuMd8dPP3AgC2o7sHCVl/yI0Dg+nGAoD4oiUFjvHy+kN0YwFA/BFS4Bivrj+UDN1YAGAjQgoc49X1h6LpxgKAeDrl8XFyjEmB4+sPafeHBhKfR9Yf8nI3FgD3Wp4E4+RoSYGjvLj+kFe7sQC41/IkGSdHSwoc57X1h/zdWPrLX15DasqZEOa2biwA7nSqinFy+pqkx/V12K2vu360pCCm6w9de2Eb89HNvyj+biwV/l24uRsLgDutTaJxcoQUIEm7sQC4U0ESjZOjuwdI0m4sAO6UmUTj5AgpQDW6sQAgUXom0Tg5unsAAHCR2kk0To6QAgCAy+QmyTg5unsAAHCh3CQYJ0dIAQDApWp7fJwcIQUAqlFMy8t/vQK2IKQAQBSSYb0UwBYMnAWACCXLeimALQgpgOW8vhS7V9ZLUXqc/z+Ac+juASxG14I710vx8kBGIJ5oSQEsRdeCXZJpvRTAUyElLy9PcnJypGnTpjJhwgTx+SJr7uzWrZukpKQEHmPGjHHicgDXo2vBPsm0XgrgmZBy4sQJGTp0qFx00UWybt062bJliyxcuLDK55WUlMj27duloKBADh8+bB7PP/98TS8H8IRkWordbeulVDTRWPe38sh6KYBnQso777wjhYWFMnPmTOnYsaNMmzZN5s+fX+XzNmzYYFpSWrRoIU2aNDGPs846q8pAVFRUFPIAvIiuBfsk03opgGdCyqZNm6RXr17SoEEDs63BQ1tTqrJ27VrZvXt3IKSMGzfOhJDKTJ8+XdLT0wOPdu3a1fTyASvRtWCnZFkvBXDd7J7hw4fLhx9+WGZ/7dq1ZcSIEYFtHVui+7T7RseoVGTbtm1y+eWXyyOPPCJHjhyR0aNHy6xZs2TSpEkVPmfy5Mly3333Bba1JYWgAi9KpqXY3SYZ1ksBbJHii3CU63fffSfHjh0rs3/27NkmmGh3j58GhzVr1kibNm0ivpBXXnlF5syZY8a1REpDiraoaHdTWlpaxM8D3DS7RwX/kvrfCvnLHYBbRfr+HXFLSsuWLcvdn5WVZWb3BCsuLpZ69epFc72SmZkpe/bsieo5QDJ0LYTXSdEWFOqkAEgGNS7mplOP582bF9jOz883Y0syMipvhu7du7f85S9/CXTXrF69Wjp06FDTywE8ha4FJBMWboTjIaVv376m2WbBggVy++23m9k9AwYMMONSlI43ady4cWDb7/zzz5exY8fK1KlTZevWrTJjxgx54YUXano5gOd4fSl2QFFdGTUak1KZN998U0aOHGmmENeqVcsMsO3S5cxUvZQUM934wgsvDHmOhhcNNStWrDBdPRMnTjQzfKLBmBQA8M74q/A3I8ZfeVek79+OhBS1f/9+Wb9+vZmO3KxZfP7qI6QAgPu7eC5/+u8VFi/0z2RbObE/XT8eEun7t2Nr9+gA2sGDB8ctoAAA3I/qyqgMCwwCABKG6sqoDCEFAJAwVFdGZQgpAJxXWCiye3f5x3S/HgdYuBFVIKTA6gF1q7cfkjc27jEfdRsuoAEkN1fkiitEdu0KPabbul+PE1TAwo2IdZ0UIBaomeBixcUiBQUi//ynyJVXiuiaX1q0UQOKbut+/3np6Ym+WliA6sqI+RTkRGAKsjdRM8EDggPJOeeILFokcuutP2/7gwsQhIqzyaMo3nVSEoGQ4j3UTPCQ8JYTRUABIAmokwI4gZoJHqJBRFtQguk2AQVAhAgpsAo1EzzWkqJdPMF0O3wwLQBUgJACq1AzwaNjUlatKv3oH0xLUAEQAUIKrELNBA/QOijBAUXHoFx6aenH4KBSUR0VADiDkAKrUDPBAxo3FsnMLDtIVj/6g4oe1/MAoBLM7oGVqJPiclqoTeugtG1b9pi2oGhAoUYKkLSKmIIMt6NmAgB4U6Tv31SchbU0kPTu2CzRlwEASBDGpAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAVqKYGwDAEVSJhtMIKQCAGmO9LcQC3T0AgBoHlHGvfhESUNT+wuNmvx4HqoOQAgCoURePtqCUt1Ktf58e1/OAaBFSAADVpmNQwltQgmk00eN6HhAtQgoAoNp0kKyT5wHBCCkAgGrTWTxOngcEI6QAAKpNpxnrLJ6KJhrrfj2u5wHRIqQAAKpN66DoNGMVHlT823qceimoDkIKgKjpTI3V2w/JGxv3mI/M3EhuWgflxVt6SFZ6aJeObut+6qSguijmBiAqFO1CefT//cAuWVSchaNSfD6fa/8EKioqkvT0dCksLJS0tLREXw6QNEW7wl80/G9D/NUMwMn3b7p7AESEol0A4s2xkHLw4EHJzs6WHTt2RPycjz76SDp37izNmzeXmTNnOnUpAGKAol0AXBlSNKAMGTIkqoBy4MABGTZsmIwcOVJWr14tr732mnzwwQdOXA6AGKBoFwBXhpQRI0bIqFGjonqOhpLWrVvLQw89JJ06dZKHH35Y5s+f78TlAIgBinYBcGVImTdvnowfPz6q52zatEn69esnKSmlQ+569uwp69evr/Q5J06cMINtgh8A4oOiXQCsDSnDhw+XJk2alHnMnTvXjEWJlgaM4Ofp6N69e/dW+pzp06eb0cD+R7t27aL+dwFUD0W7AFhbJ+Wll16SY8eOldmfkVG9v5rq1Kkjqampge369etLSUlJpc+ZPHmy3HfffSFBh6ACxL9oV3idFC3aRZ0UAAkLKS1btnT0H9Zwo4Nn/YqLi6VevXqVPkdDTXCwARB/FO0C4PmKszk5OfKnP/0psL1hwwZp06ZNoi4HQBQ0kPTu2CzRlwHA42JezE27ZE6ePFlmv04/XrVqlbz33nvm+DPPPCODBg2K9eUAAACXiHlI6datmyxbtqzMfi3gNmvWLLnmmmtMV9K2bdtkypQpsb4cAADgEglfuyc/P1+2bt0qffr0kUaNGkX1XNbuAQDAfSJ9/074Ksg6Dbk6U5gBAIC3scAgAACwEiEFAABYiZACAACsREgBAABWIqQAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAALASIQUAAFiJkAIAAKxESAEAAFYipAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArFQn0RcAAIAXnTrtk7X530tB8XHJbFxfemZnSO1aKYm+LFchpAAA4LDlefvk0be2yL7C44F9rdLry9ShXSS3a6uEXpub0N0DAIDDAWXcq1+EBBS1v/C42a/HERlCCuLS5Ll6+yF5Y+Me81G3AcCL9PVNW1DKe5Xz79PjvA5Ghu4exBRNngCSiY5BCW9BCabRRI/reb07NovrtbkRLSmIGZo8ASQbHSTr5HnJjpCCmKDJE0Ay0lk8Tp6X7AgpSHiTJwB4hU4z1i7tiiYa6349ruehaoQUxARNngCSkdZB0TF3Kjyo+Lf1OPVSIkNIQUzQ5AkgWWcJ6qSAF2/pIVnpoa9vuq37mTQQOWb3IKZNnjpItryXkpQzv7A0eQLw4ixBva6BXbKoOFtDtKQgJmjyBJDsswT19U2nGV97YRvzkde76BFSEDM0eQKoCWYJgu4exBRNngCqi8JoIKQgbk2eABANZgmC7h4AgJWYJQhCCgDAShRGAyEFAGAlZgnCsZBy8OBByc7Olh07dkT8nGHDhklKSkrgMWDAAKcuBwDgAcwSTG51nAooQ4YMiSqgqHXr1snmzZulbdu2Zrtu3bpOXA4AwEOYJZi8HAkpI0aMkFGjRslnn30W8XP27NkjPp9Punbt6sQlAAA8jFmCycmR7p558+bJ+PHjo3rO2rVr5dSpU6YVpWHDhiboHD58uNLnnDhxQoqKikIeAAAgyUPK8OHDpUmTJmUec+fONWNRorV161bp3r27LFu2TNasWSP5+fkyefLkSp8zffp0SU9PDzzatWsX9b8LAADcIcWnfS4R+O677+TYsWNl9mdkZEhaWlrpF0tJMWHj7LPPjvpCPv74Y7n++uvN+JbKWlL04actKRpUCgsLA9cAAADspu/f2thQ1ft3xGNSWrZsKbGUmZkphw4dMiEkNTW13HN0f0XHAACAtySsTsrNN98sK1euDGyvXr3aBCFCCAAAiEtI0SadkydPltl/wQUXyO9+9zsTVJYuXWrGo4wbN47/KwAAID4hpVu3bmZwbLiJEyeaY7m5uSac3H333fLggw/G+nIAAIDXBs66eeANAABw3/s3a/cAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAAPDuKshedOq0j2XBAQBIIEJKOZbn7ZNH39oi+wqPB/a1Sq8vU4d2kdyurRJ6bQAAJAu6e8oJKONe/SIkoKj9hcfNfj0OAABij5AS1sWjLSjlVbfz79Pjeh4AAIgtQkoQHYMS3oISTKOJHtfzAABAbBFSguggWSfPAwAA1UdICaKzeJw8DwAAVB8hJYhOM9ZZPBVNNNb9elzPAwAAsUVICaJ1UHSasQoPKv5tPU69FAAAYo+QEkbroLx4Sw/JSg/t0tFt3U+dFAAA4oNibuXQIDKwSxYVZwEASCBCSgU0kPTu2CzRlwEAIViyA8mEkAIALsGSHUg2jEkBABdgyQ4kI0IKAFiOJTuQrAgpAGA5luxAsiKkAIDlWLIDyYqQAgCWY8kOJCtCCgBYjiU7kKwIKQBgOZbsQLIipACAC7BkB5IRxdwAwCVYsgPJhpACAC7Ckh1IJnT3AAAAKxFSAACAlQgpAADASoQUAABgJUIKAACwEiEFAABYiZACAACsREgBAABWIqQAAAArubrirM/nMx+LiooSfSkAACBC/vdt//u4J0NKcXGx+diuXbtEXwoAAKjG+3h6enqFx1N8VcUYi50+fVr27t0rjRs3lpSUlKRMohrQdu3aJWlpaZLMuBehuB8/416E4n78jHuRuHuh0UMDSuvWraVWrVrebEnRb6xt27aS7PQHKtl/wfy4F6G4Hz/jXoTifvyMe5GYe1FZC4ofA2cBAICVCCkAAMBKhBQXS01NlalTp5qPyY57EYr78TPuRSjux8+4F/bfC1cPnAUAAN5FSwoAALASIQUAAFiJkAIAAKxESAEAAFYipLjEwYMHJTs7W3bs2BHxc4YNG2Yq8fofAwYMkGS+Hx999JF07txZmjdvLjNnzhQvyMvLk5ycHGnatKlMmDChynUw/Lp16xbyszFmzBhJpnuwZMkS6dChg6l2uXjxYvGS6twPL/081PS1wouvE9W9F1a8h+jsHtjtwIEDvksuuURfaXz5+fkRP69Vq1a+zZs3+w4fPmweR48e9SXr/SgoKPClpaX5Hn30Ud8//vEPX48ePXx///vffW52/Phx39lnn+0bO3as75tvvvFdc801vj/+8Y9VPu+HH37wNWjQwNwT/89GSUmJL1nugf5O1KtXzzdv3jzfl19+6Tv33HN9W7du9XlBde6Hl34eavpa4cXXiZq8btrwHkJIcYGrrrrKN3v27Kh+uHbv3u3LysryeVF17sesWbN8v/jFL3ynT58220uXLvWNHj3a52Z/+9vffE2bNjVvMmrjxo2+yy67rMrnrVy50terVy+fF1TnHvz2t7/1DRo0KLD93HPP+R588EFfst4PL/081PS1wouvE9W9F7a8h9Dd4wLz5s2T8ePHR/WctWvXyqlTp8zaRg0bNpQRI0bI4cOHJVnvx6ZNm6Rfv36BhSh79uwp69evFzfT76lXr17SoEGDQJP9li1bIvrZ2L17t7Ro0UKaNGki48aNkxMnTkiy3AN9Tv/+/QPbXvhZqMn98NLPQ01fK7z4OlHde2HLewghxRLDhw83LxDhj7lz55o+xGht3bpVunfvLsuWLZM1a9ZIfn6+TJ48WZL1fugKn8HP0wW0dAVtN9+LOXPmhHxP+sJau3btKl9Itm3bJpdffrmsXLlSVqxYIe+++67MmjUrDt+J88L/v0ZyD9z8sxCL++Gln4dw0b5WePlnIzvKe2HLe4irV0H2kpdeekmOHTtWZn9GRka1vp7+MAX/QD377LNy/fXXyx/+8AdJxvtRp06dkHLP9evXl5KSEnHzvZg9e3bgL77w70sHTVYk/Gfg4YcfNoFn0qRJ4jbh/18juQdu/lmIxf3w0s9DTXn5Z8Ot7yGEFEu0bNkypl8/MzNTDh06ZJpxbVubIR73Q8PNgQMHAtvFxcVSr149cYOK7kVWVpaZyRGsOt+X/mzs2bNH3Ej/v0Z7D9z8sxCL++Gln4ea8vLPhlvfQ+ju8aibb77ZNN/6rV692rzZuSGgxIJOydR74LdhwwZp06aNeOl70uZYfQGpqrWpd+/esmvXrsC2fg2djpss98CLPws1uR9e+nmoKS//bLj2PSTRI3cRufJGZRcWFvp+/PHHMuc+/vjjvosvvtj3ySefmBH/LVu29D3yyCO+ZL0fOv2ufv36vnfffdccz83N9d1zzz0+Nzt58qSvRYsWgSmmY8aM8Q0ZMiRwXKcM/vTTT2Wed8cdd/iuvvpq35o1a3wLFy70NWzY0Hz02j2o6PvXGS/6Pev04+LiYt+FF17o+/d//3efF1Tnfnjp5yHS14pkep2o7r2w5T2EkOLyN+UOHTqYH6Bw+kP361//2rzg6DQynfevL2DJej/Uiy++6Ktbt66Zopmdne3bv3+/z+3eeOMNU+OiWbNm5s3pq6++Crk/GzZsKPMcfbMaPny476yzzjL36z/+4z98XrwHFX3/6oEHHjC1UrQmxkUXXeSZuiDVuR9e+3mI5LUi2V4nqnMvbHkPSdH/xLftBkgcbf7WUet9+vSRRo0aiRfs37/fTJPUqafNmjWTZFSde6BTc3XsxRVXXOG5cQf8TNSMF18n3IqQAgAArMTAWQAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAViKkAAAAKxFSAACAlQgpAADASoQUAAAgNvp/lls0AKiaWU4AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=3\n",
      "预测结果：\n",
      "[2 2 2 2 0 0 0 0 2 0 0 0 2 2 0 2 2 0 0 0 2 2 0 1 1 2 1 1 2 1]\n",
      "SSE: 74.84977274579333\n",
      "轮廓系数: 0.2902178743546057\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=4\n",
      "预测结果：\n",
      "[2 2 3 3 3 0 0 0 3 0 0 0 3 3 0 3 3 0 0 0 3 2 1 1 1 2 1 1 2 1]\n",
      "SSE: 71.53927634613459\n",
      "轮廓系数: 0.43242207027751906\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
